RACK1 is an interaction partner of ATG5 and a novel regulator of autophagy

Autophagy is biological mechanism allowing recycling of long-lived proteins, abnormal protein aggregates, and damaged organelles under cellular stress conditions. Following sequestration in double- or multimembrane autophagic vesicles, the cargo is delivered to lysosomes for degradation. ATG5 is a key component of an E3-like ATG12-ATG5-ATG16 protein complex that catalyzes conjugation of the MAP1LC3 protein to lipids, thus controlling autophagic vesicle formation and expansion. Accumulating data indicate that ATG5 is a convergence point for autophagy regulation. Here, we describe the scaffold protein RACK1 (receptor activated C-kinase 1, GNB2L1) as a novel ATG5 interactor and an autophagy protein. Using several independent techniques, we showed that RACK1 interacted with ATG5. Importantly, classical autophagy inducers (starvation or mammalian target of rapamycin blockage) stimulated RACK1-ATG5 interaction. Knockdown of RACK1 or prevention of its binding to ATG5 using mutagenesis blocked autophagy activation. Therefore, the scaffold protein RACK1 is a new ATG5-interacting protein and an important and novel component of the autophagy pathways

Structural host-microbiota interaction networks.

Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences

Subversion of TLR pathway by various bacterial and viral proteins at several steps.

<p>This is a simplified representation of TLR pathway where the orange nodes are the host proteins and red nodes are the microbial proteins.</p

Structural HMI network.

<p><b>(a)</b> High-resolution metaorganism network where grey nodes represent host proteins, red nodes microbial proteins, black edges host PPIs, and red edges HMIs. If an exogenous interface–HMI–(red edges) overlaps endogenous ones, it can abolish the endogenous PPI. If the exogenous and endogenous interfaces do not overlap, then the HMI does not disrupt the endogenous PPI. Without 3D structure knowledge of PPIs and HMIs, we cannot infer whether the interfaces overlap or not. <b>(b)</b> Zoom-in views of blue boxes show whether the interfaces overlap. <b>(c)</b> 3D representations of the interactions shown in part (b). Above diagram shows the superimposed view of 4mi8:AC and 2p1l:AB where Gamma Herpesvirus vBcl2 (red) and human Bcl-XL (green) binds to the same site on human Beclin-1 (grey). Here, an exogenous interface mimics an endogenous interface. Below diagram shows the superimposed view of 1f5q:AB and 1buh:AB, where Gama Herpesvirus Cyclin (red) and human CKS1 (green) bind to distinct interfaces on human CDK2 (grey).</p

Distinct microbial compositions may lead to different outcomes (hypothetical scenario).

<p>Combinatorial effects of microbial effectors and the active host pathways determine the cell response. <b>(a)</b> Composition1 has certain microorganisms that secrete effector protein combination1. These effectors activate pathway1 in the host, which produces pro-inflammatory cytokines. <b>(b)</b> Composition2 secretes effector combination2 and activates pathway2 in addition to pathway1. Additive effects of these two pathways amplifies the signal and promotes inflammation (cross-activation). <b>(c)</b> Microbial composition3 utilize effector combination3 to activate both pathway 1 and 3, which have opposing outcomes. Subtractive effects of these pathways result in no inflammation (cross-inhibition).</p

Structural Pathways of Cytokines May Illuminate Their Roles in Regulation of Cancer Development and Immunotherapy

Cytokines are messengers between tissues and the immune system. They play essential roles in cancer initiation, promotion, metastasis, and immunotherapy. Structural pathways of cytokine signaling which contain their interactions can help understand their action in the tumor microenvironment. Here, our aim is to provide an overview of the role of cytokines in tumor development from a structural perspective. Atomic details of protein-protein interactions can help in understanding how an upstream signal is transduced; how higher-order oligomerization modes of proteins can influence their function; how mutations, inhibitors or antagonists can change cellular consequences; why the same protein can lead to distinct outcomes, and which alternative parallel pathways can take over. They also help to design drugs/inhibitors against proteins de novo or by mimicking natural antagonists as in the case of interferon-γ. Since the structural database (PDB) is limited, structural pathways are largely built from a series of predicted binary protein-protein interactions. Below, to illustrate how protein-protein interactions can help illuminate roles played by cytokines, we model some cytokine interaction complexes exploiting a powerful algorithm (PRotein Interactions by Structural Matching—PRISM)

Endogenous (intra-species) and exogenous (inter-species) interface mimicry.

<p><b>(a)</b> A, B, C, D are host proteins and P is pathogenic protein. Protein A has two interfaces: through blue interface it binds to B and through grey interface it binds to C and D. C and D proteins employ similar interfaces to bind to A. So, endogenous interfaces mimic each other. Pathogenic protein P has similar interface as B and competes to bind to the blue interface on A. In this case, an exogenous interface mimics an endogenous interface. <b>(b)</b> The F1L protein of variola virus interacts with human BID protein (5ajj:AB.pdb) and inhibits apoptosis in the host cell by hijacking the interface between human BID-BCLXL (4qve:AB.pdb): an exogenous interface mimicking an endogenous one. Human MCL1 protein binds to human BID (5c3f:AB.pdb) in a very similar fashion that BCLXL does: endogenous interfaces mimicking each other.</p

Identification of Interconnected Markers for T-Cell Acute Lymphoblastic Leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is a complex disease, resulting from proliferation of differentially arrested immature T cells. The molecular mechanisms and the genes involved in the generation of T-ALL remain largely undefined. In this study, we propose a set of genes to differentiate individuals with T-ALL from the nonleukemia/healthy ones and genes that are not differential themselves but interconnected with highly differentially expressed ones. We provide new suggestions for pathways involved in the cause of T-ALL and show that network-based classification techniques produce fewer genes with more meaningful and successful results than expression-based approaches. We have identified 19 significant subnetworks, containing 102 genes. The classification/prediction accuracies of subnetworks are considerably high, as high as 98%. Subnetworks contain 6 nondifferentially expressed genes, which could potentially participate in pathogenesis of T-ALL. Although these genes are not differential, they may serve as biomarkers if their loss/gain of function contributes to generation of T-ALL via SNPs. We conclude that transcription factors, zinc-ion-binding proteins, and tyrosine kinases are the important protein families to trigger T-ALL. These potential disease-causing genes in our subnetworks may serve as biomarkers, alternative to the traditional ones used for the diagnosis of T-ALL, and help understand the pathogenesis of the disease

Identification of Interconnected Markers for T-Cell Acute Lymphoblastic Leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is a complex disease, resulting from proliferation of differentially arrested immature T cells. The molecular mechanisms and the genes involved in the generation of T-ALL remain largely undefined. In this study, we propose a set of genes to differentiate individuals with T-ALL from the nonleukemia/healthy ones and genes that are not differential themselves but interconnected with highly differentially expressed ones. We provide new suggestions for pathways involved in the cause of T-ALL and show that network-based classification techniques produce fewer genes with more meaningful and successful results than expression-based approaches. We have identified 19 significant subnetworks, containing 102 genes. The classification/prediction accuracies of subnetworks are considerably high, as high as 98%. Subnetworks contain 6 nondifferentially expressed genes, which could potentially participate in pathogenesis of T-ALL. Although these genes are not differential, they may serve as biomarkers if their loss/gain of function contributes to generation of T-ALL via SNPs. We conclude that transcription factors, zinc-ion-binding proteins, and tyrosine kinases are the important protein families to trigger T-ALL. These potential disease-causing genes in our subnetworks may serve as biomarkers, alternative to the traditional ones used for the diagnosis of T-ALL, and help understand the pathogenesis of the disease

RACK1 is an interaction partner of ATG5 and a novel regulator of autophagy

Autophagy is biological mechanism allowing recycling of long-lived proteins, abnormal protein aggregates and damaged organelles under cellular stress conditions. Following sequestration in double or multimembrane autophagic vesicles, the cargo is delivered to lysosomes for degradation. ATG5 is a key component of an E3-like ATG12-ATG5-ATG16 protein complex that catalyzes conjugation of the MAP1LC3 protein to lipids, thus controlling autophagic vesicle formation and expansion. Accumulating data indicate that ATG5 is a convergence point for autophagy regulation. Here, we describe the scaffold protein RACK1 (Receptor Activated C-Kinase 1, GNB2L1), as a novel ATG5 interactor and an autophagy protein. Using several independent techniques, we showed that RACK1 interacted with ATG5. Importantly, classical autophagy inducers (starvation or mTOR blockage) stimulated RACK1-ATG5 interaction. Knockdown of RACK1 or prevention of its binding to ATG5 using mutagenesis blocked autophagy activation. Therefore, the scaffold protein RACK1 is a new ATG5-interacting protein and an important and novel component of the autophagy pathways